Contents

1 Executive Summary

In this report we investigate the statistical techniques used by the forensic services at Police Scotland on blood alcohol computations in criminal cases associated with drink driving. In UK law, it is an offence to drive with blood alcohol concentration (BAC) above the prescribed limit (0.47g/kg). Since alcohol levels fall over time, the measured BAC at the police station is lower than at the time of driving; forensic scientists ‘back-calculate’ the earlier level using an elimination rate (Forensic Science Regulator 2020).

The current method implemented by Police Scotland incorporates the \(\beta\) elimination rate, a measure of how quickly alcohol is eliminated from the bloodstream. A fixed range for \(\beta\) is applied across all individuals, using the 2.5th percentile of the distribution as a conservative cutoff. If the back‑calculated BAC exceeds the legal limit under this assumption, the individual is reported as over the limit. While simple, this binary approach does not reflect the variability of \(\beta\) across individuals or the uncertainty inherent in the calculation.

Using the provided dataset of 100 individuals (Armbrecht 2008), we examined how \(\beta\) varies based on individual characteristics (weight, height, age, sex). We found that there us significant negative correlations between \(\beta\)and both weight and height, a clear sex-specific difference, with males typically showing high elimination rates. Further, we found age has little influence on \(\beta\).

We then applied these findings by comparing several modelling strategies:

In our investigation, we propose replacing the current binary cutoff with a probabilistic and interval-based framework:

As a case study example, we found that under the current approach, an individual would be deemed not over the limit, while our suggested approaches provide respective probabilities of 76% and 75% that the individual was indeed over the limit - revealing a substantial likelihood that the individual was above the legal threshold, in contrast to the binary conclusion under current practice.

We also considered situations where no blood sample is available and Widmark’s equation must be used along with eyewitness dose, which introduces the volume of distribution \(V_d\). The standard approach treats \(\beta\) and \(V_d\) as independent and applies fixed percentile ranges for both.

We found \(V_d\) and \(\beta\) to be negatively correlated, meaning treating them as independent and combining extreme marginal percentiles can lead to implausible scenarios. Hence, we suggest reports should acknowledge this relationship and avoid the assumption of independence.

In conclusion, our findings support a shift towards a probabilistic reporting framework in forensic alcohol analysis. We recommend expert witness reports should include:

This approach ultimately enhances transparency, communicates uncertainty, and provides courts with a mode scientifically defensible basis for decision making.

2 Investigating the \(\beta\) Elimination Rate

2.1 Introduction

The \(\beta\) elimination rate (measured in g/kg/hr) measures how quickly alcohol is eliminated from the bloodstream, and is used by forensic scientists at Police Scotland when ‘back-calculating’ the BAC at the time of offence (Forensic Science Regulator 2020). This calculation is of form

\[C_0 = C_t +\beta t,\]

where \(C_t\) is the measured BAC at time \(t\) hours after the alleged offence, and \(C_0\) is the estimated BAC at the time of driving.

Currently, forensic scientists use a single, fixed range of \(\beta\) values for all individuals. This approach simplifies practical implementation but does not account for variability due to characteristics of the individual. In particular, factors such as age, gender, weight, or height could influence the rate at which alcohol is eliminated from the bloodstream.

Hence, it is worth exploring whether \(\beta\) can be more effectively modelled to reflect the variability in individual’s characteristics, rather than relying on a fixed population range.

2.2 Provided Data

The dataset analysed in this study was taken from a PhD thesis, and contains measurements associated with BAC (Armbrecht 2008). In particular, the dataset contains 100 observations with characteristic information about the individual, and details about the dose of alcohol consumed by the individual.

Key variables that are explored in this report include:

  • Beta60: the negative of the \(\beta\) elimination rate (g/kg/hr), though for our analysis we took the corresponding positive variable beta;

  • C0: the concentration of alcohol in the blood at time zero;

  • weight: the weight (in kg) of a given individual;

  • height: the height (in cm) of a given individual;

  • age: the age (in years) of a given individual;

  • sex: the sex of a given individual;

  • Amount of alcohol consumed: the dose (in g) of alcohol consumed.

2.3 Exploratory Data Analysis

In order to understand and evaluate the performance of the \(\beta\) elimination rate, we first examine various plots to visualise how the rate is influenced by certain characteristics. The characteristics of interest are weight, height, age, and sex.

These are each plotted against the \(\beta\) elimination rate. Intuitively a larger \(\beta\) value corresponds to faster elimination of alcohol from the blood stream. Figures 1-4 below explore these relationships.

From Figures 1-4, we can deduce the following:

  • as weight increases the elimination rate decreases linearly;

  • similarly, as height increases the elimination rate decreases linearly;

  • the majority of age data is scattered between 20-30, however there is a reasonably constant spread of values, suggesting age is less influential on alcohol elimination;

  • there is a clear difference in elimination rate between genders, with males typically obtaining higher values.

To further enhance our investigation into the effect each of these characteristics has on the elimination rate, the following table evaluates the significance of correlation between each characteristic and \(\beta\).

Table 1: Correlations Between Beta and Each Characteristic (with P-values)
Characteristic Correlation P_value
age 0.041 0.68772
height -0.410 0.00002
weight -0.356 0.00027

From this output, we can see that weight and height have significant correlations with the \(\beta\) elimination rate, with respective correlations of -0.356 and -0.410. This suggests that these characteristics play a crucial role in determining the rate at which alcohol is eliminated from the blood stream. Further, the correlation test returned a p-value below 0.05 for both of these characteristics, indicating that there is evidence to reject the null hypothesis that there is no correlation between these characteristics and \(\beta\). As expected, there is a low correlation between age and elimination rate, and a large p-value of 0.68772 further validates this. Hence, we can deduce that age plays little role in the elimination rate of alcohol from the blood stream.

Due to the clear relationships between \(\beta\) and characteristics of the tested individuals, it is worth considering approaches in which these factors are incorporated to accurately predict the rate at which alcohol is eliminated from the blood stream.

2.4 Modelling Beta

2.4.1 Finding Distribution Method

An issue with creating a distribution from the \(\beta\) elimination rate values given is that we only have 100 data points. Therefore the distribution is very dependent on not many values. We propose instead investigating if \(\beta\) follows a standard distribution. First, we will examined the general spread of \(\beta\) values given in the following figure, where the mean, 2.5th, and 97.5th quantiles are indicated in red.

We have seen from exploratory data analysis that the rate at which alcohol density in the blood decreases is dependent on a range of variables given in the data. As Sex is the only discrete variable, we can split the data in two to see how \(\beta\), the alcohol elimination rate, is distributed for males and females in the dataset given.

Thesefigures can point us towards a few different distributions. We can analyse how well the data fits the distributions in the following plots.

2.4.1.1 Residual Plots of Fitted Distributions

The aim of finding a distribution to fit \(\beta\) is to smooth out the distribution to decrease the influence of a few outliers in the original dataset, as there are only 100 values. That means that a single datapoint can skew the 2.5th quantile that we are aiming to find. We are also focused on simplicity, so have only looked at 3 different distributions that have the general shape of the data, shown in the figures in the section above. Our method selection also relies on simplicity, as we do not want to over-fit our chosen distribution on such a small dataset. To evaluate how well our data fits our chosen distributions, we have analysed the residual (\(\epsilon_i\)) graphs below.

2.4.1.2 Normal Distribution

  • Empirical and Theoretical Densities: The theoretical peak value is skewed compared to the empirical peak, and has lower density.

  • Q-Q plot: Lack-of-fit observed at distribution tails. The left tail is heavy while the right tail is light.

  • Empirical and Theoretical CDFs: Poor fit at central values of the distribution.

  • P-P plot: Lack-of-fit at the distribution centre, as the central values deviate from the linear relationship.

2.4.1.3 Beta Distribution

  • Empirical and Theoretical Densities: Improved fit of peak is observed, while still not completely aligned.

  • Q-Q plot: Points are randomly distributed above and below the general linear trend, with outliers at the lower tail.

  • Empirical and Theoretical CDFs: Improved fit at distribution centre compared to normal.

  • P-P plot: Improved fit at distribution centre compared to normal.

2.4.1.4 Gamma Distribution

  • Empirical and Theoretical Densities: Best fit of peak of the analysed distributions.

  • Q-Q plot: Points are randomly distributed above and below the general linear trend across all quantiles.

  • Empirical and Theoretical CDFs: Best fit of theoretical distribution to data.

  • P-P plot: Majority of points lie on fitted line.

After looking at the residuals, we conclude that Gamma is the most appropriate distribution for our \(\beta\) with the fitting method.

The 2.5th quantile in the gamma distribution of \(\beta\) is 0.1257847. The density graph of the gamma distribution across the whole population of Gamma is shown below to demonstrate fit, again with the mean, 2.5th, and 97.5th quantiles indicated in red.

Considering that Sex is a factor variable, we can find the distributions of beta_60 dependent on whether the suspect is male of female.

The 2.5% quantile in the gamma distribution for males is 0.1219521, and for females it is 0.1430605.

2.4.2 Linear Model Method

Instead of fitting a distribution, we can create a linear model. This creates a distribution for \(\beta\) given a range of information about the suspect, whether this information is in the form of discrete or continuous form. This is more useful than the method that finds a different beta distribution for males and females, as it is not possible to do the same for continuous information such as weight. Therefore the previous methods don’t make the most of all the information given.

We can create the following model, where \(\beta_j\) represent the coefficients for \(j \in \{0, ..., 4\}\) to model \(Y_i\), the \(i\) datapoints of \(\beta\):

\[ \begin{aligned} \text{linear model}: \quad Y_i &= \beta_0 + \beta_1\text{weight}_i + \beta_2\text{age}_i + \beta_3\text{height}_i + \beta_4\text{sex}_i + \epsilon_i, \\ \epsilon_i &\overset{\mathrm{iid}}\sim \mathcal{N}(0, \sigma^2). \end{aligned} \]

  • Residual vs Fitted: There is a change of variance with mean, so the constant variance assumption is slightly violated. Unfortunately there is little data points to establish if this is an important violation.

  • Q-Q Residuals: The ordered standardised residuals are plotted against quantiles of a standard normal. Lack-of-fit observed at distribution tails. The left tail is heavy while the right tail is light.

  • Scale-Location: We have random variation around the mean, so there is no violation of the constant variation assumption.

  • Residuals vs Leverage: This plot gives us Cook’s distance, which measures the change in all model fitted values on omission of the data point in question. From the plot we can see there are no outliers, so there aren’t any highly influential points in our data.

To try to resolve these violations of the assumptions on residuals, we can create an alternative linear model. Given that there is a quadratic shape in the Residuals vs Fitted graph, we create the following linear model.

\[ \begin{aligned} \text{square root linear model}: \quad \sqrt{Y_i} &= \beta_0 + \beta_1\text{weight}_i + \beta_2\text{age}_i + \beta_3\text{height}_i + \beta_4\text{sex}_i + \epsilon_i, \\ \epsilon_i &\overset{\mathrm{iid}}\sim \mathcal{N}(0, \sigma^2). \end{aligned} \]

We get the following residual graphs.

The Q-Q Residuals plot is improved, however there is only marginal change in the Residuals vs Fitted plot. Therefore we explore alternative methods.

2.4.3 Quantile Linear Model Method

The biggest issue with the linear model above for use in our project is that it’s aim is to find the conditional mean function, \(E(\beta | x)\), where \(x\) represents the explanatory variables of the model. The aim of our analysis is to find the 2.5% quantile of \(\beta\). It is more accurate to use quantile regression (Rodriguez and Yao 2017). We use the same linear model as above.

Quantile of whole distribution of y|x, instead of quantile of expectation of y|x.

While it may look like taking the 2.5% quantile of the linear model, and using the quantile regression model produce very similar results, the methods are different. To find the 2.5% quantile of the linear model, we find the 2.5% quantile of the residuals, and add that onto \(E(\beta | x)\) predicted by our model. This assumes homoscedaticity, that the residuals are independent of predictors, and we have shifted the line uniformly, without taking into account how residual quantile could change across the data (Rodriguez and Yao 2017). Our quantile regression method does not have a homoscedaticity assumption, which is more reasonable, especially as the residual plots of our linear model are questionable in upholding the homoscedaticity assumption.

2.6 Applying Various Approaches

The following figure compares the approaches we have discussed above, that is

  • the current empirical (population) based approach, with a binary decision;

  • our suggested Gamma distribution approach, with a probabilistic decision approach, and corresponding confidence interval;

  • our suggested alternative quantile regression approach, also with a probabilistic decision approach, and corresponding confidence interval.

As illustrated in Figure , there is a great deal of overlap between our two recommended approaches, with both providing probabilities of above around 75% that the individual was above the legal limit at the time of driving. The narrower confidence interval provided by quantile regression indicates a decrease in uncertainty when this approach is taken.

Compared to the binary approach currently implemented by Police Scotland, these recommended approaches provide a measure of the uncertainty in such a procedure, and account for this in their probabilistic nature.

Thus, we recommend to Police Scotland that, when dealing with cases as illustrated in this case study, a probabilistic approach is incorporated to account for uncertainty in the back calculation approach. Especially when cases provide borderline estimates (as illustrated in this case study), it is crucial that these estimates are treated with the uncertainty that is associated with their derivation.

3 Investigating Volume of Distribution

3.1 Introduction

If it is too late for a blood or breath test to predict accurate results for \(C_0\), but there is eyewitness evidence measuring the quantity of alcohol consumed, the Widmark’s equation can help to calculate the blood alcohol concentration \(t\) hours after drinking (\(C_t\)). The validity of this estimate is dependent on the reliability of the eyewitness’ testimony, however this analysis can be combined with other evidence to support a case. Widmark’s equation can be written as

\[ C_t = \frac{A}{\text{Weight} \times V_d} - \beta t, \]

where \(A\) is the observed does of alcohol consumed, \(\text{Weight}\) is the weight of the individual, and \(V_d\) is a parameter known as the volume of distribution, “a proportionality constant that relates the total amount of drug (in our case alcohol) in the body to the plasma concentration of the drug at a given time.” (Mansoor and Mahabadi 2023) \(V_d\) is measured with the units L/kg. Rearranging this equation we find

\[ C_0 = C_t +\beta_t = \frac{A}{\text{Weight} \times V_d}, \]

which in turn implies

\[ V_d = \frac{A}{\text{Weight} \times C_0}. \]

Hence, given we are provided the data for \(A\), \(\text{Weight}\), and \(C_0\), we can calculate \(V_d\) using the above equation.

3.2 Exploratory Data Analysis

Similar to our approach in analysing the effectiveness of the \(\beta\) elimination rate, we can investigate the relationships between \(V_d\) and characteristics of individuals (weight, height, age, and sex). It is also worth investigating the distribution of \(V_d\) values across our sample.

Figures 14-18 help us visualise these relationships.

From Figure 5, we can see that the majority of \(V_d\) values lie around 0.65, in a roughly normally distributed pattern, with the mean, 2.5th, and 97.5th quantiles highlighted in red. There appears to be one particular outlier, with a \(V_d\) value of around 1.2, which may be worth investigating further.

Figures 6-9 highlight the following relationships:

  • \(V_d\) shows a very slight negative trend as weight increases;

  • \(V_d\) appears constant across the range of heights;

  • \(V_d\) appears to decrease with age, although again the majority of the data lies between ages of 20-30, so general conclusions are hard to draw from the sample;

  • \(V_d\) appears constant across both genders.

The above findings suggest that \(V_d\) seems to differ little across various characteristics of the individual. This suggests \(V_d\) is a more generalised approach compared to the \(\beta\) elimination rate.

It is also worth considering how the two rates are correlated, as independence is assumed between \(\beta\) and \(V_d\). Performing a correlation test between the two can help to deduce whether this assumption is upheld. Further, Figure 19 helps to visualise the relationship between \(\beta\) and \(V_d\).

From the correlation test, we find that there is an observed correlation of -0.404, and the null hypothesis that the true correlation is equal to zero, i.e. the two measures are independent, can be confidently rejected as there is a corresponding p-value of 3.122x10⁻⁵.

Figure 10 further highlights this clear relationship between the two measures, and hence the independence assumption appears to be violated.

3.2.1 Advantages of Current Method

The current method offers several key strengths that make it well-suited for forensic applications. Its straightforward approach is both easy to implement and readily explained in court proceedings, which is essential for ensuring that evidence is understood by legal professionals and juries. By using the 97.5th percentiles, the method takes a conservative stance that provides defendants with the benefit of the doubt, aligning with fundamental principles of justice. The standardised nature of this approach ensures consistency across all cases, preventing arbitrary variations in how evidence is evaluated. Furthermore, the methodology’s transparency and reproducibility give it scientific rigour, allowing independent verification of results and supporting its credibility in legal contexts.

3.2.2 Limitations of Current Method

The current forensic method assumes independence between \(\beta\) and \(V_d\) when calculating the range for \(C_t\). However, a Pearson’s product-moment correlation analysis reveals a statistically significant negative correlation between these two parameters (correlation coefficient = -0.404, p-value = 3.122x10⁻⁵ < 0.05). This finding directly contradicts the independence assumption underlying the current approach.

Although the official forensic procedure specifies using the 97.5th percentile for both \(\beta\) and \(V_d\), this convention is based on policy rather than mathematical reasoning. Forensic laboratories apply the same “upper percentile rule” across all parameters to maintain consistency and to err on the side of caution, as using higher parameter values for \(\beta\) and \(V_d\) generally produces lower blood alcohol estimates. Mathematically Widmark’s equation shows that \(C_t\) decreases as \(\beta\) and \(V_d\) both increase, thus the maximum possible value of \(C_t\) would occur for low \(\beta\) and low \(V_d\) values (their 2.5th percentiles). Regardless of which tail is used, the critical statistical issue with the current method is that it assumes that \(\beta\) and \(V_d\) are independent, when our analysis shows that \(\beta\) and \(V_d\) have a significant negative correlation.

Assuming independence allows forensic analysts to combine their most extreme values (e.g. highest \(\beta\) with highest \(V_d\)) even though such an extreme combination almost never occurs in practice. This may produce inaccurate \(C_t\) estimates, exaggerating how high a person’s blood alcohol concentration might have been at the time of interest or significantly underestimating. In statistical terms, the current approach treats the joint distribution of \((\beta, V_d)\) as the product of their marginal distributions, when in reality the joint distribution should account for their negative dependence structure.

The above plot of the joint distribution between \(\beta\) and \(V_d\) shows that there are no points in the top-right corner where both parameters are at their 97.5th marginal quantile simultaneously.

3.2.3 Alternative Approach: Empirical Joint Distribution

##     2.5%      50%    97.5% 
## 0.658526 1.014769 1.455071
Table 5:Comparison of Empirical Joint vs Independent Ct
Method Lower_2.5 Median Upper_97.5
Empirical joint (β,Vd) 0.659 1.015 1.455
Independent 97.5th percentiles 0.497

The empirical joint \(C_t\) distribution represents the upper and lower bounds of plausible blood alcohol concentrations for an individual with weight 70kg, who consumed the mean grams of alcohol from the sample and was tested 2 hours later.

As seen from the table above this resulted in a range (0.659, 1.455), compared to \(C_t\) when assuming independence of \(\beta\) and \(V_d\) which gave a value of 0.497 which is outside the range of plausible values computed using the joint distribution. This highlights the idea that the current method may be too conservative, resulting in underestimated values of the actual blood alcohol levels of a person at the time of interest.

4 Bibliography

Armbrecht, Fee-Antoinette. 2008. “Über Die Möglichkeiten Der Forensischen Rückrechnung Anhand von Atemalkoholkonzentrationen Im Vergleich Zu Blutalkoholkonzentrationen - Eine Prospektive Vergleichende Untersuchung Der Kinetiken von 100 Probanden.” Ludwig-Maximilliants-Universität München.
Forensic Science Regulator. 2020. “Guidance: Alcohol Back Calculation for Road Traffic Investigations, FSR-g-220 Issue 2.” Forensic Science Regulator.
Mansoor, Asad, and Navid Mahabadi. 2023. “Volume of Distribution.” StatPearls [Internet]. U.S. National Library of Medicine. https://www.ncbi.nlm.nih.gov/books/NBK545280/.
Rodriguez, R., and Y. Yao. 2017. “Five Things You Should Know about Quantile Regression.” Proceedings of the SAS Global Forum 2017, Paper SAS525-2017.

5 Code Appendix

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(afex))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(lubridate))
suppressPackageStartupMessages(library(viridis))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(patchwork))
suppressPackageStartupMessages(library(kableExtra))
suppressPackageStartupMessages(library(cv))
suppressPackageStartupMessages(library(stringr))
suppressPackageStartupMessages(library(fitdistrplus))
suppressPackageStartupMessages(library(readxl))
suppressPackageStartupMessages(library(quantreg))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(broom))

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TASK 1  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

data <- read_excel("SCS_BAC_and_BrAC_split_TOP.xlsx")
data$sex <- as.factor(data$Sex)
data$beta60 <- data$`Beta60 (g/kg/h)`
data$weight <- data$`Weight (kg)`
data$height <- data$`Height (cm)`
data$age <- data$`Age (years)`
data$beta <- -data$beta60

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%% BETA EDA %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# beta vs weight
weight_plot <- ggplot(data, aes(x=weight, y=beta, colour = sex)) +
  geom_point(size = 3) +
  geom_smooth(method="lm", color="navy") +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  labs(title="Figure 1: β vs Weight", x="Weight (kg)", y="β Elimination Rate (g/kg/h)")

# beta vs height
height_plot <- ggplot(data, aes(x=height, y=beta, colour = sex)) +
  geom_point(size = 3) +
  geom_smooth(method="lm", color="navy") +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  labs(title="Figure 2: β vs Height", x="Height (cm)", y="β Elimination Rate (g/kg/h)")

# beta vs age
age_plot <- ggplot(data, aes(x=age, y=beta, colour = sex)) +
  geom_point(size = 3) +
  geom_smooth(method="lm", color="navy") +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  labs(title="Figure 3: β vs Age", x="Age (years)", y="β Elimination Rate (g/kg/h)")

# beta vs gender
sex_plot <- ggplot(data, aes(x = sex, y = beta60,
                             fill = sex)) +
  geom_violin(alpha = 0.8) +
  geom_boxplot(width = 0.2, color = "navy", alpha = 0.7) +
  scale_fill_manual(values = c("seagreen", "skyblue")) +
  labs(
    title = "Figure 4: β vs Gender",
    x = "Gender",
    y = "β Elimination Rate (g/kg/h)"
  ) +
  guides(fill = "none")

# Compute correlation and p-value between beta and each characteristic.
corr_table <- data %>%
  dplyr::select(beta, weight, age, height) %>%
  pivot_longer(cols = c(weight, age, height), names_to = "Characteristic", values_to = "Value") %>%
  group_by(Characteristic) %>%
  summarise(
    Correlation = cor(beta, Value, use = "pairwise.complete.obs"),
    P_value = cor.test(beta, Value)$p.value
  ) %>%
  mutate(
    Correlation = round(Correlation, 3),
    P_value = signif(P_value, 3)
  )

#%%%%%%%%%%%%%%%%%%%% BETA DISTRIBUTION TESTING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Density plot of beta.
density_plot <- ggplot(data, aes(x = beta)) +
  geom_histogram(aes(y = after_stat(density)),
                 binwidth = 0.005,
                 fill = "steelblue",
                 alpha = 0.55) +
  geom_density(color = "navy", 
               size = 1) +
  geom_vline(aes(xintercept=mean(beta)),
             linetype = "dashed",
             color = "red",
             size = 2) +
  geom_vline(aes(xintercept=quantile(beta, 0.025)),
             linetype = "dotted",
             color = "red",
             size = 2) +
  geom_vline(aes(xintercept=quantile(beta, 0.975)),
             linetype = "dotted",
             color = "red",
             size = 2) +
  labs(x = expression(""*beta), y = "Density",
       title = expression("Distribution of "*beta))

# compute group statistics
stats <- data %>%
  group_by(sex) %>%
  summarize(
    mean = mean(beta),
    q025 = quantile(beta, 0.025),
    q975 = quantile(beta, 0.975)
  )

# helper: color mapping for male/female lines
stats <- stats %>%
  mutate(line_colour = ifelse(sex == "female", "red", "blue"))

# custom legend labels for quantile lines
legend_lines <- data.frame(
  sex = c("male", "female"),
  color = c("blue", "red"),
  linetype = c("dotted", "dotted"),
  label = c("97.5th quantile (male)", "97.5th quantile (female)")
)

# main plot
sex_density_plot <- ggplot(data, aes(x = beta, fill = sex)) +
  geom_histogram(aes(y = after_stat(density), color = sex),
                 binwidth = 0.005, alpha = 0.5, position = "identity") +
  geom_density(aes(color = sex), size = 1, alpha = 0) +
  
  # mean lines (dashed, red/blue)
  geom_vline(data = stats,
             aes(xintercept = mean, color = line_colour, linetype = "Mean"),
             size = 2) +
  
  # lower quantile (dotted)
  geom_vline(data = stats,
             aes(xintercept = q025, color = line_colour, linetype = "2.5th quantile"),
             size = 2) +
  
  # upper quantile (dotted)
  geom_vline(data = stats,
             aes(xintercept = q975, color = line_colour, linetype = "97.5th quantile"),
             size = 2) +
  
  # consistent manual color/fill mapping
  scale_fill_manual(values = c("male" = "skyblue", "female" = "seagreen"),
                    name = "Sex") +
  scale_color_manual(
    name = "Quantile Colour",
    values = c("male" = "skyblue", "female" = "seagreen",
               "red" = "red", "blue" = "blue"),
    breaks = c("blue", "red"),
    labels = c("male quantiles (blue)", "female quantiles (red)")
  ) +
  scale_linetype_manual(
    name = "Statistic",
    values = c("Mean" = "dashed", "2.5th quantile" = "dotted", "97.5th quantile" = "dotted")
  ) +
  labs(
    x = expression(beta),
    y = "Density",
    title = expression("Distribution of "*beta*" by Sex")
  )

# Residual plots of fitted distributions
normal_fit <- fitdist(data$beta, distr = "norm", method = "mle")
beta_fit <- fitdist(data$beta, distr = "beta", method = "mle")
gamma_fit <- fitdist(data$beta, distr = "gamma", method = "mle")

# plot each fitted distribution on residual plot
plot.legend <- c("Normal", "Beta", "Gamma")
denscomp(list(normal_fit, beta_fit, gamma_fit), legendtext = plot.legend)
qqcomp(list(normal_fit, beta_fit, gamma_fit), legendtext = plot.legend)
cdfcomp(list(normal_fit, beta_fit, gamma_fit), legendtext = plot.legend)
ppcomp(list(normal_fit, beta_fit, gamma_fit), legendtext = plot.legend)

# modelling as gamma and finding 2.5% quantile
gamma_fit <- fitdist(-data$beta60, distr = "gamma", method = "mle")
summary(gamma_fit)
par(mar=c(1, 1, 1, 1))
plot(gamma_fit)
gamma_shape <- gamma_fit$estimate[[1]]
gamma_rate <- gamma_fit$estimate[[2]]
q025 <- -qgamma(0.975, gamma_shape, gamma_rate)

gamma_df <- data.frame(
  x = data$beta,
  density = dgamma(data$beta, shape = gamma_shape, rate = gamma_rate)
)

quantiles <- qgamma(c(0.025, 0.5, 0.975), gamma_shape, gamma_rate)

stats_gamma <- data.frame(
  Quantile = rep(c("q025", "q50", "q975")),
  value = quantiles
)

# plot gamma fit for beta.
gamma_plot <- ggplot(data, aes(x = beta)) +
  geom_histogram(
    aes(y = after_stat(density)),
    binwidth = 0.005,
    fill = "steelblue",
    alpha = 0.5,
    position = "identity") +
  geom_line(
    data = gamma_df,
    aes(x = x,
        y = density
    ),
    color = "navy",
    size = 1.2) +
  geom_vline(
    data = stats_gamma,
    aes(xintercept = value,
        linetype = Quantile),
    color = "red",
    size = 2
  ) +
  scale_linetype_manual(
    values = c("q025" = "dotted", "q50" = "dashed", "q975" = "dotted"),
    labels = c("2.5%", "50%", "97.5%")
  ) +
  labs(
    x = expression(beta),
    y = "Density",
    title = expression("Gamma Distribution of "*beta),
    linetype = "Quantile"
  ) 

m_data <- data %>% filter(Sex == "male")
m_gamma_fit <- fitdist(m_data$beta, distr = "gamma", method = "mle")
m_gamma_shape <- m_gamma_fit$estimate[[1]]
m_gamma_rate <- m_gamma_fit$estimate[[2]]
m_quantiles <- qgamma(c(0.025, 0.5, 0.975), m_gamma_shape, m_gamma_rate)

f_data <- data %>% filter(Sex == "female")
f_gamma_fit <- fitdist(f_data$beta, distr = "gamma", method = "mle")
f_gamma_shape <- f_gamma_fit$estimate[[1]]
f_gamma_rate <- f_gamma_fit$estimate[[2]]
f_quantiles <- qgamma(c(0.025, 0.5, 0.975), f_gamma_shape, f_gamma_rate)

gamma_sex_df <- data.frame(
  x = data$beta,
  male_density   = dgamma(data$beta, shape = m_gamma_shape, rate = m_gamma_rate),
  female_density = dgamma(data$beta, shape = f_gamma_shape, rate = f_gamma_rate)
)

gamma_sex_long <- gamma_sex_df %>%
  pivot_longer(cols = c(male_density, female_density),
               names_to = "Sex",
               values_to = "density") %>%
  mutate(Sex = ifelse(Sex == "male_density", "male", "female"))

stats_gamma_sex <- data.frame(
  Sex = rep(c("male", "female"), each = 3),
  Quantile = rep(c("q025", "q50", "q975"), times = 2),
  value = c(m_quantiles, f_quantiles)
)

# gamma fit of beta by sex
sex_gamma_plot <- ggplot(data, aes(x = beta)) +
  # histogram with correct fill colors
  geom_histogram(
    aes(y = after_stat(density), fill = Sex),
    binwidth = 0.005, alpha = 0.5, position = "identity"
  ) +
  
  # gamma density curves (match fill colours)
  geom_line(
    data = gamma_sex_long,
    aes(x = x, y = density, color = Sex, linetype = "Gamma density"),
    size = 1.2
  ) +
  
  # quantile lines (male = blue, female = red)
  geom_vline(
    data = stats_gamma_sex %>%
      mutate(line_colour = ifelse(Sex == "female", "red", "blue")),
    aes(xintercept = value, color = line_colour, linetype = Quantile),
    size = 2
  ) +
  
  # color scales (fill = distribution colour, line = quantile/gamma)
  scale_fill_manual(
    values = c("male" = "skyblue", "female" = "seagreen"),
    name = "Sex"
  ) +
  scale_color_manual(
    name = "Quantile Colour",
    values = c("male" = "skyblue", "female" = "seagreen",
               "red" = "red", "blue" = "blue"),
    breaks = c("skyblue", "seagreen", "blue", "red"),
    labels = c("Male gamma curve", "Female gamma curve",
               "Male quantiles (blue)", "Female quantiles (red)")
  ) +
  
  # line type labels for quantiles
  scale_linetype_manual(
    name = "Statistic",
    values = c("Gamma density" = "solid",
               "q025" = "dotted",
               "q50"  = "dashed",
               "q975" = "dotted"),
    labels = c("Gamma density" = "Gamma density curve",
               "q025" = "2.5th quantile",
               "q50"  = "50th quantile (median)",
               "q975" = "97.5th quantile")
  ) +
  
  labs(
    x = expression(beta),
    y = "Density",
    title = expression("Gamma Distributions of "*beta*" by Sex")
  )

#%%%%%%%%%%%%%%%%%%%%%%% LINEAR MODELLING BETA %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# --- linear modelling beta 60 attempt ---

# model incorporating all characteristics
lm_model <- lm(beta ~ weight + height + sex, data = data)

# Residual plots
par(mfrow = c(2,2))
plot(lm_model)

# Try modelling square root of beta (residual plots indicate
# a slight quadratic mean-variance relationship)

sqrt_lm_model <- lm(sqrt(beta) ~ weight + height + sex, data = data)

# Residual plots
par(mfrow = c(2,2))
plot(sqrt_lm_model)

# compute group statistics
stats <- data %>%
  group_by(Sex) %>%
  summarize(
    mean = mean(beta60),
    q025 = quantile(beta60, 0.025),
    q975 = quantile(beta60, 0.975)
  )

# fit models
lm_model <- lm(beta ~ weight + height + sex, data = data)
rq_model <- rq(beta ~ weight + height + sex, data = data, tau = 0.025)

# predictions for linear model
pred_int <- predict(lm_model, newdata = data, interval = "prediction", level = 0.95)

# create df for comparing predicted vs actual
pred_data <- data %>%
  mutate(
    lm_pred_mean = pred_int[, "fit"],   # mean OLS prediction
    lm_pred_lwr  = pred_int[, "lwr"],   # 2.5% prediction interval (lower)
    lm_pred_upr  = pred_int[, "upr"]    # 97.5% prediction interval (upper)
  )

# add empirical 2.5% quantile of residuals to find 2.5% quantile of linear model
resid_q025 <- quantile(residuals(lm_model), probs = 0.025)
pred_data <- pred_data %>%
  mutate(lm_q025 = lm_pred_mean + resid_q025)

# quantile regression predictions
pred_data$rq_pred <- predict(rq_model, newdata = data)

# reshape
plot_data <- pred_data %>%
  pivot_longer(
    cols = c(lm_pred_mean, lm_q025, rq_pred),
    names_to = "model",
    values_to = "predicted_beta"
  )

# plot
quantile_v_lm_plot <- ggplot() +
  # 95% prediction interval shaded area (between lm_pred_lwr and lm_pred_upr)
  geom_ribbon(
    data = pred_data,
    aes(x = beta, ymin = lm_pred_lwr, ymax = lm_pred_upr),
    fill = "lightblue", alpha = 0.25
  ) +
  # Points for models (mean LM, quantile LM, quantile regression)
  geom_point(
    data = plot_data,
    aes(x = beta, y = predicted_beta, color = model),
    alpha = 0.6, size = 2
  ) +
  # Linear trend lines for clarity
  geom_smooth(
    data = plot_data,
    aes(x = beta, y = predicted_beta, color = model),
    method = lm, se = TRUE, linewidth = 1
  ) +
  # 1:1 line
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
  # Manual colors and labels
  scale_color_manual(
    values = c(
      "lm_pred_mean" = "blue",
      "lm_q025"      = "gold",
      "rq_pred"      = "darkred"
    ),
    labels = c(
      "lm_pred_mean" = "Linear Model (Mean Prediction)",
      "lm_q025"      = "2.5% Quantile of Linear Model Residuals",
      "rq_pred"      = "Quantile Regression (τ = 0.025)"
    )
  ) +
  # Labels and theme
  labs(
    title = expression("Figure 12: Actual vs Predicted " * beta * " with 95% Prediction Interval"),
    x = expression("Actual " * beta),
    y = expression("Predicted " * beta),
    color = "Model Type"
  )


# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TASK 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Given parameters
Ct <- 0.15         # measured concentration (g/kg)
t  <- 2            # time since arrest (hours)
x  <- 0.47         # legal limit (g/kg)

# Fitted Gamma parameters for beta
gamma_shape <- 31.9538
gamma_rate  <- 173.7079

# 1. Compute 2.5th and 97.5th percentiles of beta
beta_q025 <- qgamma(0.025, shape = gamma_shape, rate = gamma_rate)
beta_q975 <- qgamma(0.975, shape = gamma_shape, rate = gamma_rate)

# 2. Compute 95% interval for C0
C0_lower <- Ct + beta_q025 * t
C0_upper <- Ct + beta_q975 * t
C0_interval <- c(C0_lower, C0_upper)

# 3. Compute probability that true C0 exceeded legal limit
beta_threshold <- (x - Ct) / t
p_over_limit <- 1 - pgamma(beta_threshold, shape = gamma_shape, rate = gamma_rate)

# 4. Monte Carlo simulation
set.seed(123)
n_sim <- 1000
beta_samples <- rgamma(n_sim, shape = gamma_shape, rate = gamma_rate)
C0_samples <- Ct + beta_samples * t
p_over_limit_sim <- mean(C0_samples > x)

# Summaries
C0_mean <- mean(C0_samples)
C0_sd   <- sd(C0_samples)

# Create summary table
results <- data.frame(
  Statistic = c(
    "Interval length",
    "Lower 95% bound for C₀",
    "Upper 95% bound for C₀",
    "Analytical P(C₀ > 0.47 g/kg)",
    "Simulated P(C₀ > 0.47 g/kg)",
    "Mean of simulated C₀",
    "SD of simulated C₀"
  ),
  Value = c(
    round(C0_upper - C0_lower, 3),
    round(C0_lower, 3),
    round(C0_upper, 3),
    round(p_over_limit, 3),
    round(p_over_limit_sim, 3),
    round(C0_mean, 3),
    round(C0_sd, 3)
  )
)

kable(
  results,
  caption = "Table 2: Summary of Back-Calculated Blood-Alcohol Concentration (C₀) Using Gamma Approach",
  col.names = c("Statistic", "Value"),
  align = c("l", "c"),
  format = "markdown"
)

# Fit quantile regression models for beta
rq_lower  <- rq(beta ~ weight + age + sex, data = data, tau = 0.025)
rq_upper  <- rq(beta ~ weight + age + sex, data = data, tau = 0.975)

# Predict quantile values of beta for each observation
beta_q025 = predict(rq_lower,  newdata = data)
beta_q975 = predict(rq_upper,  newdata = data)

# Compute 95% interval for C0 for each observation
C0_lower = Ct + mean(beta_q025) * t
C0_upper = Ct + mean(beta_q975) * t
C0_interval <- c(C0_lower, C0_upper)

# Monte Carlo-style resampling from the quantile predictions
set.seed(123)
n_sim <- 1000
# approximate sampling of beta between 2.5% and 97.5% quantiles
beta_samples <- runif(n_sim, min = mean(beta_q025), max = mean(beta_q975))
C0_samples <- Ct + beta_samples * t

# Probability that C0 exceeded the legal limit x
p_over_limit_sim <- mean(C0_samples > x)

# Summaries
C0_mean <- mean(C0_samples)
C0_sd   <- sd(C0_samples)

# Create summary table
results <- data.frame(
  Statistic = c(
    "Interval Length",
    "Lower 95% bound for C₀",
    "Upper 95% bound for C₀",
    "Simulated P(C₀ > 0.47 g/kg)",
    "Mean of simulated C₀",
    "SD of simulated C₀"
  ),
  Value = c(
    round(C0_upper - C0_lower, 3),
    round(C0_lower, 3),
    round(C0_upper, 3),
    round(p_over_limit_sim, 3),
    round(C0_mean, 3),
    round(C0_sd, 3)
  )
)

kable(
  results,
  caption = "Table 3: Summary of Back-Calculated Blood-Alcohol Concentration using Quantile Regression (C₀)",
  col.names = c("Statistic", "Value"),
  align = c("l", "c"),
  format = "markdown"
)

# Parameters
gamma_shape <- 31.9538
gamma_rate  <- 173.7079

# Quantiles for beta
beta_q025 <- qgamma(0.025, shape = gamma_shape, rate = gamma_rate)
beta_q975 <- qgamma(0.975, shape = gamma_shape, rate = gamma_rate)

# Case-specific parameters
C_t <- 0.15
t <- 2
C_legal <- 0.47

# Derived beta that yields legal limit for C0
beta_limit <- (C_legal - C_t) / t

# Sequence of beta values
beta <- seq(beta_q025, beta_q975, length.out = 1000)

# Corresponding C0 values
C0 <- C_t + beta * t

# Gamma PDF for beta
pdf_beta <- dgamma(beta, shape = gamma_shape, rate = gamma_rate)

# Plot C0 vs beta
plot(beta, C0, type = "l", lwd = 2, col = "blue",
     xlab = expression(beta), ylab = expression(C[0]),
     main = expression(paste("Figure 13: BAC at C_0 vs Elimination Rate (", beta, ")")))

# Add horizontal line for legal limit on C0 scale
abline(h = C_legal, col = "darkgreen", lwd = 2, lty = 2)

# Add vertical line at beta corresponding to legal limit
abline(v = beta_limit, col = "darkgreen", lwd = 2, lty = 2)

# Add marker for (beta_limit, C0 = 0.47)
points(beta_limit, C_legal, pch = 19, col = "darkgreen", cex = 1.5)
text(beta_limit, C_legal - 0.015, labels = expression(paste("(", beta[limit], ", ", C[legal], ")")), 
     pos = 4, col = "darkgreen")

# --- Overlay PDF curve (secondary y-axis) ---
par(new = TRUE)
plot(beta, pdf_beta, type = "n", axes = FALSE, xlab = "", ylab = "", ylim = c(0, max(pdf_beta)))

# Shade area under the PDF curve for beta > beta_limit
shade_idx <- beta >= beta_limit
polygon(c(beta[shade_idx], rev(beta[shade_idx])),
        c(pdf_beta[shade_idx], rep(0, sum(shade_idx))),
        col = rgb(1, 0, 0, 0.3), border = NA)

# Draw the full PDF line
lines(beta, pdf_beta, col = "red", lwd = 2)

# # Add secondary axis for density
# axis(side = 4, col = "red", col.axis = "red")
# mtext("Density of β", side = 4, line = 3, col = "red")

# Add legend
legend("topleft",
       legend = c(expression(C[0] == C[t] + beta*t),
                  expression("Legal limit"),
                  expression("P(C"[0]*"> limit)")),
       col = c("blue", "darkgreen", "red"),
       lwd = c(2, 2, NA),
       pch = c(NA, NA, 15),
       pt.cex = 1.5,
       bty = "n",
       cex = 0.9,
       fill = c(NA, NA, rgb(1,0,0,0.3)))


# 1. Current approach (fixed beta)
beta_fixed <- 0.126
C0_fixed   <- Ct + beta_fixed * t
p_fixed    <- as.numeric(C0_fixed > x_limit) 

# 2. Gamma model (use report MLEs)
gamma_shape <- 31.9538
gamma_rate  <- 173.7079

beta_q025_gamma <- qgamma(0.025, shape = gamma_shape, rate = gamma_rate)
beta_q975_gamma <- qgamma(0.975, shape = gamma_shape, rate = gamma_rate)

C0_gamma_lower <- Ct + beta_q025_gamma * t
C0_gamma_upper <- Ct + beta_q975_gamma * t
C0_gamma_mean  <- mean(c(C0_gamma_lower, C0_gamma_upper))

# Probability analytically from Gamma CDF
beta_threshold <- (x_limit - Ct) / t
p_gamma <- 1 - pgamma(beta_threshold, shape = gamma_shape, rate = gamma_rate)

# 3. Quantile regression approach (population-average bands)
# Fit lower/upper quantile regression for beta
rq_lower <- rq(beta ~ weight + age + sex, data = data, tau = 0.025)
rq_upper <- rq(beta ~ weight + age + sex, data = data, tau = 0.975)

# Predict β quantiles for each observation in the dataset
beta_q025_qr <- as.numeric(predict(rq_lower, newdata = data))
beta_q975_qr <- as.numeric(predict(rq_upper, newdata = data))

# Summarize Co interval by averaging β quantiles across the population 
C0_qr_lower <- Ct + mean(beta_q025_qr, na.rm = TRUE) * t
C0_qr_upper <- Ct + mean(beta_q975_qr, na.rm = TRUE) * t
C0_qr_mean  <- mean(c(C0_qr_lower, C0_qr_upper))

p_qr <- p_over_limit_sim

# plot
results_df <- tibble(
  Method = c("Current (Fixed β=0.126)", "Gamma model", "Quantile regression"),
  Lower  = c(C0_fixed, C0_gamma_lower, C0_qr_lower),
  Upper  = c(C0_fixed, C0_gamma_upper, C0_qr_upper),
  Mean   = c(C0_fixed, C0_gamma_mean, C0_qr_mean),
  Prob   = c(p_fixed, p_gamma, p_qr)
)

# Plot
p <- ggplot(results_df, aes(y = Method)) +
  # Interval bands (thin lines)
  geom_segment(aes(x = Lower, xend = Upper, y = Method, yend = Method,
                   colour = Method), linewidth = 2) +
  # Endpoints for clarity
  geom_point(aes(x = Lower, colour = Method), size = 3) +
  geom_point(aes(x = Upper, colour = Method), size = 3) +
  # Mean point
  geom_point(aes(x = Mean, colour = Method), size = 10, shape = 18) +
  # Legal limit line
  geom_vline(xintercept = x_limit, linetype = "dashed",
             colour = "darkgreen", linewidth = 1) +
  annotate("text", x = x_limit + 0.01, y = 3.3,
           label = "Legal limit = 0.47 g/kg",
           colour = "darkgreen", hjust = 0, size = 9) +
  annotate("rect", xmin = x_limit - 0.005, xmax = x_limit + 0.005,
           ymin = 0.5, ymax = 3.5, alpha = 0.1, fill = "darkgreen") +
  # Probability labels (skip Current approach)
  # Probability labels (skip Current approach)
  geom_text(
    data = subset(results_df, Method != "Current (Fixed β=0.126)"),
    aes(x = Upper, 
        y = Method, 
        label = sprintf("P(C[0] > 0.47) == %.3f", Prob)),
    parse = TRUE,
    hjust = 1,
    vjust = 1.5,
    size = 9,
    colour = "black"
  ) +
  scale_x_continuous(name = expression(C[0]~"(g/kg)"),
                     limits = c(0.35, 0.72),
                     expand = c(0, 0)) +
  scale_colour_manual(values = c("Current (Fixed β=0.126)" = "grey60",
                                 "Gamma model"             = "firebrick",
                                 "Quantile regression"     = "hotpink"),
                      name = "Method") +
  ggtitle(expression("Comparison of reporting frameworks for " * C[0]))

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TASK 3 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# define A, Co, Vd
data$A <- data$`Amount of Alcohol Consumed (g)`
data$Co <- data$`Co (g/Kg)`
data$Vd <- data$A/(data$Co *data$weight)

# view summary and quantiles of Vd
summary(data$Vd)
quantile(data$Vd, probs = c(0.025, 0.5, 0.975))

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Vd EDA %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# histogram of Vd
density_plot <- ggplot(data, aes(x = Vd)) +
  geom_histogram(aes(y = after_stat(density)),
                 binwidth = 0.005,
                 fill = "steelblue",
                 alpha = 0.55) +
  geom_density(color = "navy", 
               size = 1) +
  geom_vline(aes(xintercept=mean(Vd)),
             linetype = "dashed",
             color = "red",
             size = 2) +
  geom_vline(aes(xintercept=quantile(Vd, 0.025)),
             linetype = "dotted",
             color = "red",
             size = 2) +
  geom_vline(aes(xintercept=quantile(Vd, 0.975)),
             linetype = "dotted",
             color = "red",
             size = 2) +
  labs(x = "Vd (L/kg)", y = "Density", title = expression("Distribution of Vd"))

# Vd vs weight
weight_plot2 <- ggplot(data, aes(x = weight, y = Vd, colour = sex)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", colour = "navy") +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  labs(title = "Figure 6: Vd vs Weight", x = "Weight (kg)", y = "Vd (L/kg)")

# Vd vs height
height_plot2 <- ggplot(data, aes(x = height, y = Vd, colour = sex)) +
  geom_point(size = 3) +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  geom_smooth(method = "lm", colour = "navy") +
  labs(title = "Figure 7: Vd vs Height", x = "Height (cm)", y = "Vd (L/kg)")

# Vd vs age
age_plot2 <- ggplot(data, aes(x = age, y = Vd, colour = sex)) +
  geom_point(size = 3) +
  scale_colour_manual(values = c("male" = "skyblue", "female" = "seagreen")) +
  geom_smooth(method = "lm", colour = "navy") +
  labs(title = "Figure 8: Vd vs Age", x = "Age (years)", y = "Vd (L/kg)")

# Vd vs gender
sex_plot2 <- ggplot(data, aes(x = sex, y = Vd,
                              fill = sex)) +
  geom_violin(alpha = 0.8) +
  geom_boxplot(width = 0.2, color = "navy", alpha = 0.7) +
  scale_fill_manual(values = c("seagreen", "skyblue")) +
  labs(
    title = "Figure 9: Vd vs Gender",
    x = "Gender",
    y = "Vd (L/kg)"
  ) +
  guides(fill = "none")

# Test correlation to investigate independent assumption
cor.test(data$beta, data$Vd, use = "complete.obs")

# View quantiles of each coefficient for comparison
beta_range <- quantile(data$beta, probs = c(0.025, 0.975))
Vd_range   <- quantile(data$Vd, probs = c(0.025, 0.975))

# Scatter plot between beta and V_d
ggplot(data, aes(x = beta, y = Vd, colour = sex)) +
  geom_point() +
  scale_colour_manual(values = c("male" = "steelblue", "female" = "lightblue")) +
  geom_smooth(method = "lm", color = "navy") +
  labs(
    title = "Figure 10: Relationship between β and Vd",
    subtitle = paste("Correlation =", round(cor(data$beta, data$Vd, use = "complete.obs"), 3)),
    x = "β elimination rate (g/kg/h)",
    y = "Vd (L/kg)"
  )

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ALT APPROACH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Visualize the joint distribution

ggplot(data, aes(x = beta, y = Vd)) +
  geom_point(alpha = 0.6, color = "orange", size = 3) +
  geom_density_2d(color = "red") +
  # Add the marginal 97.5th percentiles
  geom_vline(xintercept = quantile(data$beta, 0.975, na.rm = TRUE), 
             linetype = "dashed", color = "blue", linewidth = 1) +
  geom_hline(yintercept = quantile(data$Vd, 0.975, na.rm = TRUE), 
             linetype = "dashed", color = "blue", linewidth = 1) +
  labs(
    title = "Joint Distribution of β and Vd",
    subtitle = "Blue lines show marginal 97.5th percentiles",
    x = "β (g/kg/h)",
    y = "Vd (L/kg)"
  ) +
  annotate("text", 
           x = quantile(data$beta, 0.975, na.rm = TRUE), 
           y = min(data$Vd, na.rm = TRUE),
           label = "97.5th percentile β", size = 7, 
           hjust = -0.1, color = "blue")


# Test person (from task 2)
A <- mean(data$A) # Chose this kinda randomly but made the most sense to me 
weight <- 70
t <- 2

# Calculate Ct using empirical joint distribution
data$Ct_joint <- (A / (weight * data$Vd)) - data$beta * t

# Compute the quantiles of C_t
quantile(data$Ct_joint, probs = c(0.025, 0.5, 0.975), na.rm = TRUE)

# Current (independent) approach
beta_ind <- quantile(data$beta, 0.975, na.rm = TRUE)
Vd_ind   <- quantile(data$Vd, 0.975, na.rm = TRUE)
Ct_independent <- (A / (weight * Vd_ind)) - beta_ind * t

# Table comparison
results_compare <- tibble(
  Method = c("Empirical joint (β,Vd)", "Independent 97.5th percentiles"),
  Lower_2.5 = c(round(quantile(data$Ct_joint, 0.025, na.rm = TRUE), 3), ""),
  Median     = c(round(quantile(data$Ct_joint, 0.5, na.rm = TRUE), 3), ""),
  Upper_97.5 = c(round(quantile(data$Ct_joint, 0.975, na.rm = TRUE), 3), 
                 round(Ct_independent, 3))
)